Fairness on the web

Kunegis, Jérôme; Preusse, Julia

doi:10.1145/2380718.2380741

Cited by 28 publications

(3 citation statements)

References 16 publications

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“…To quantify the centralization of research communities C for each claim, we employed the Gini coefficient. The Gini coefficient is used to measure the heterogeneity of distributions in social and information networks (Kunegis and Preusse, 2012). The coefficient ranges between 0 and 1.…”

Section: Methodsmentioning

confidence: 99%

“…The Gini coefficient can be also represented as a percentage ranging from 0 to 100, as in Figure 2D–E. While other measures of network centralization are available (e.g., Freeman’s centralization [Freeman, 1978]), the Gini coefficient, and the Lorenz curve on which it is based, is independent from the underlying degree distribution, making it suitable for comparisons among networks with different size and mean degree (Kunegis and Preusse, 2012; Badham, 2013).…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Centralized scientific communities are less likely to generate replicable results

Danchev

Rzhetsky

Evans

2019

eLife

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Concerns have been expressed about the robustness of experimental findings in several areas of science, but these matters have not been evaluated at scale. Here we identify a large sample of published drug-gene interaction claims curated in the Comparative Toxicogenomics Database (for example, benzo(a)pyrene decreases expression of SLC22A3) and evaluate these claims by connecting them with high-throughput experiments from the LINCS L1000 program. Our sample included 60,159 supporting findings and 4253 opposing findings about 51,292 drug-gene interaction claims in 3363 scientific articles. We show that claims reported in a single paper replicate 19.0% (95% confidence interval [CI], 16.9–21.2%) more frequently than expected, while claims reported in multiple papers replicate 45.5% (95% CI, 21.8–74.2%) more frequently than expected. We also analyze the subsample of interactions with two or more published findings (2493 claims; 6272 supporting findings; 339 opposing findings; 1282 research articles), and show that centralized scientific communities, which use similar methods and involve shared authors who contribute to many articles, propagate less replicable claims than decentralized communities, which use more diverse methods and contain more independent teams. Our findings suggest how policies that foster decentralized collaboration will increase the robustness of scientific findings in biomedical research.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Centralized scientific communities are less likely to generate replicable results

Danchev

Rzhetsky

Evans

2019

eLife

View full text Add to dashboard Cite

show abstract

“…we use the Shannon's Entropy measure in its normalized version (Shannon, 1951). It is worth noting that other measures of heterogenity like the Gini coefficient (Kunegis and Preusse, 2012), could be used to pursue such a scope. The entropy of the actual networks N (p) and N (d) is tested against a set of 100 random networks per actual network, with the same number of nodes and links.…”

Section: Complexity Of Nmentioning

confidence: 99%